Effects of hydration status during heat acclimation on plasma volume and performance
Bibliographic record
Abstract
The impact of hydration status was investigated during a 5‐day heat acclimation ( HA ) training protocol vs mild/cool control conditions on plasma volume ( PV ) and performance (20 km time‐trial [ TT ]). Sub‐elite athletes were allocated to one of two heat training groups (90 min/day): (a) dehydrated to ~2% body weight ( BW ) loss in heat (35°C; DEH ; n = 14); (b) euhydrated heat (35°C; EUH ; n = 10), where training was isothermally clamped to 38.5°C core temperature ( T c ). A euhydrated mild control group (22°C; CON ; n = 9) was later added, with training clamped to the same relative heart rate (~75% HR max ) as elicited during DEH and EUH ; thus all groups experienced the same internal training stress (% HR max ). Five‐day total thermal load was 30% greater ( P < 0.001) in DEH and EUH vs CON . There were significant differences in the average percentage of maximal work rate (%W max ) across all groups ( DEH : 24 ± 6%; EUH : 34 ± 9%; CON : 48 ± 8%W max ) during training required to elicit the same % HR max (77 ± 4% HR max ). There were no significant differences pre‐to post‐ HA between groups for PV ( DEH : +1.7 ± 10.1%; EUH : +4.8 ± 10.2%; CON : +5.2 ± 4.0%), but there was a significant pooled group PV increase, as well as a 97% likely pooled improvement in TT performance ( DEH : −1.8 ± 2.8%; EUH : −1.9 ± 2.1%, CON ; −1.8 ± 2.8%; P = 0.136). Due to a lack of between‐group differences for PV and TT , but pooled group increases in PV and 97% likely group increase in TT performance, over 5 days of intense training at the same average relative cardiac load suggests that overall training stress may also impact significant adaptations beyond heat and hydration stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".